{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pyreadstat import pyreadstat\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import plotly.express as px\n",
    "import mytools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 =pd.read_excel(R\"新媒体视域下国产美妆产品消费者行为分析.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df1[df1.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['序号', '提交答卷时间', '所用时间', '来源', '来源详情', '来自IP', '1、您的性别是',\n",
       "       '2、您开始使用化妆品的年龄', '3、您使用国货彩妆的频率', '4、您的国货产品多久换一次', '5、您一般如何了解到国货美妆品牌',\n",
       "       '6、您常使用何种国货美妆品牌', '7、您在购买国货美妆时考虑的因素排序', '8、在购买国货美妆产品时，您偏爱如何购买',\n",
       "       '9、您常购买的国货美妆产品的价格是', '10、您觉得国货美妆产品的发展还存在什么问题', '11、您认为中国美妆产品进入海外的优势',\n",
       "       '12、你认为中国美妆市场的竞争现状', '13、您认国货美妆品牌进入海外市场的劣势是',\n",
       "       '14、您认为近年来国货美妆崛起的主要原因是什么？(欢迎积极回答)\\n'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[df1['来自IP'].duplicated()]['来自IP']\n",
    "df2 = df1.drop_duplicates(subset=['来自IP'],keep='first')\n",
    "df3 = df2.rename(columns={\n",
    "     '1、您的性别是':'性别',\n",
    "     '2、您开始使用化妆品的年龄' : '开始使用化妆品的年龄',\n",
    "    '3、您使用国货彩妆的频率' : '使用国货彩妆频率',\n",
    "    '4、您的国货产品多久换一次' : '国货更换时间',\n",
    "    '5、您一般如何了解到国货美妆品牌' : '如何了解到国货',\n",
    "     '6、您常使用何种国货美妆品牌':'国货美妆的品牌',\n",
    "     '7、您在购买国货美妆时考虑的因素排序':'国货美妆的质量',\n",
    "     '8、在购买国货美妆产品时，您偏爱如何购买':'国货美妆购买渠道',\n",
    "     '9、您常购买的国货美妆产品的价格是':'国货美妆的价格',\n",
    "     '10、您觉得国货美妆产品的发展还存在什么问题':'国货美妆的问题',\n",
    "     '11、您认为中国美妆产品进入海外的优势':'国货美妆出口优势',\n",
    "     '12、你认为中国美妆市场的竞争现状':'国货美妆竞争现状',\n",
    "     '13、您认国货美妆品牌进入海外市场的劣势是':'国货美妆出口劣势',\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.query(\"国货更换时间 == '其他〖不用〗'\")\n",
    "df3.loc[69, '国货更换时间'] = '其他〖不清楚〗' \n",
    "df3.query(\"国货更换时间 == '其他〖看情况〗'\")\n",
    "df3.loc[22, '国货更换时间'] = '其他〖不清楚〗' \n",
    "df3.query(\"国货更换时间 == '其他〖看使用频率〗'\")\n",
    "df3.loc[109, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖用完才换〗'\")\n",
    "df3.loc[32, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖用完就换，或过期才换〗'\")\n",
    "df3.loc[66, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖用完就换 没有固定时间〗'\")\n",
    "df3.loc[17, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖用完了或者有好的就换〗'\")\n",
    "df3.loc[125, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖没有规律 想换就换〗'\")\n",
    "df3.loc[27, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖有喜欢的才买〗'\")\n",
    "df3.loc[134, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖无〗'\")\n",
    "df3.loc[41, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖2个月左右〗'\")\n",
    "df3.loc[59, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖对象要就买〗'\")\n",
    "df3.loc[130, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖完了就换〗'\")\n",
    "df3.loc[10, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖坏了就换〗'\")\n",
    "df3.loc[93, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖啥时候用完啥时候换〗'\")\n",
    "df3.loc[134, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖两年〗'\")\n",
    "df3.loc[94, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖很少用，所以只买过一次〗'\")\n",
    "df3.loc[83, '国货更换时间'] = '其他〖不清楚〗'\n",
    "df3.query(\"国货更换时间 == '其他〖啥时候用完啥时候换〗'\")\n",
    "df3.loc[113, '国货更换时间'] = '其他〖不清楚〗'\n",
    "# 如何了解到国货的修改\n",
    "df3.query(\"如何了解到国货 == '品牌推荐'\")\n",
    "df3.loc[42, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.loc[64, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.loc[84, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.loc[92, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.loc[114, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.query(\"如何了解到国货 == '品牌推荐┋代购网购'\")\n",
    "df3.loc[73, '如何了解到国货'] = '微博，小红书，抖音等APP推荐┋代购网购'\n",
    "df3.loc[80, '如何了解到国货'] = '微博，小红书，抖音等APP推荐┋代购网购'\n",
    "df3.loc[119, '如何了解到国货'] = '微博，小红书，抖音等APP推荐┋代购网购'\n",
    "df3.loc[122, '如何了解到国货'] = '微博，小红书，抖音等APP推荐┋代购网购'\n",
    "df3.query(\"如何了解到国货 == '电视广告'\")\n",
    "df3.loc[89, '如何了解到国货'] = '微博，小红书，抖音等APP推荐┋电视广告'\n",
    "df3.loc[131, '如何了解到国货'] = '微博，小红书，抖音等APP推荐┋电视广告'\n",
    "df3.query(\"如何了解到国货 == '其他〖不了解〗'\")\n",
    "df3.loc[93, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.query(\"如何了解到国货 == '其他〖不用〗'\")\n",
    "df3.loc[69, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.query(\"如何了解到国货 == '其他〖从女朋友嘴里〗'\")\n",
    "df3.loc[130, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.query(\"如何了解到国货 == '其他〖小文章〗'\")\n",
    "df3.loc[10, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推荐'\n",
    "df3.query(\"如何了解到国货 == '其他〖无〗'\")\n",
    "df3.loc[41, '如何了解到国货'] = '品牌推荐┋微博，小红书，抖音等APP推推荐'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['填写问卷时长'] = df3['所用时间'].str.rstrip('秒')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>提交答卷时间</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所用时间</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>来源</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>来源详情</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>来自IP</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>开始使用化妆品的年龄</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>使用国货彩妆频率</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货更换时间</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>如何了解到国货</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的品牌</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的质量</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆购买渠道</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的价格</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的问题</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆出口优势</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆竞争现状</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆出口劣势</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14、您认为近年来国货美妆崛起的主要原因是什么？(欢迎积极回答)\\n</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>填写问卷时长</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0\n",
       "序号                                   int64\n",
       "提交答卷时间                              object\n",
       "所用时间                                object\n",
       "来源                                  object\n",
       "来源详情                                object\n",
       "来自IP                                object\n",
       "性别                                  object\n",
       "开始使用化妆品的年龄                          object\n",
       "使用国货彩妆频率                            object\n",
       "国货更换时间                              object\n",
       "如何了解到国货                             object\n",
       "国货美妆的品牌                             object\n",
       "国货美妆的质量                             object\n",
       "国货美妆购买渠道                            object\n",
       "国货美妆的价格                             object\n",
       "国货美妆的问题                             object\n",
       "国货美妆出口优势                            object\n",
       "国货美妆竞争现状                            object\n",
       "国货美妆出口劣势                            object\n",
       "14、您认为近年来国货美妆崛起的主要原因是什么？(欢迎积极回答)\\n  object\n",
       "填写问卷时长                              object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     138\n",
       "unique     92\n",
       "top        91\n",
       "freq        5\n",
       "Name: 填写问卷时长, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['填写问卷时长'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>int64</td>\n",
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       "      <td>object</td>\n",
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       "    <tr>\n",
       "      <th>来源</th>\n",
       "      <td>object</td>\n",
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       "      <td>object</td>\n",
       "    </tr>\n",
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       "      <th>来自IP</th>\n",
       "      <td>object</td>\n",
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       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
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       "      <th>开始使用化妆品的年龄</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>使用国货彩妆频率</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货更换时间</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>如何了解到国货</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的品牌</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的质量</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆购买渠道</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的价格</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆的问题</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆出口优势</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆竞争现状</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国货美妆出口劣势</th>\n",
       "      <td>category</td>\n",
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       "    <tr>\n",
       "      <th>14、您认为近年来国货美妆崛起的主要原因是什么？(欢迎积极回答)\\n</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>填写问卷时长</th>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0\n",
       "序号                                     int64\n",
       "提交答卷时间                                object\n",
       "所用时间                                  object\n",
       "来源                                    object\n",
       "来源详情                                  object\n",
       "来自IP                                  object\n",
       "性别                                  category\n",
       "开始使用化妆品的年龄                          category\n",
       "使用国货彩妆频率                            category\n",
       "国货更换时间                              category\n",
       "如何了解到国货                             category\n",
       "国货美妆的品牌                             category\n",
       "国货美妆的质量                             category\n",
       "国货美妆购买渠道                            category\n",
       "国货美妆的价格                             category\n",
       "国货美妆的问题                             category\n",
       "国货美妆出口优势                            category\n",
       "国货美妆竞争现状                            category\n",
       "国货美妆出口劣势                            category\n",
       "14、您认为近年来国货美妆崛起的主要原因是什么？(欢迎积极回答)\\n    object\n",
       "填写问卷时长                                 int32"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.astype({\n",
    " '填写问卷时长': 'int',\n",
    " '性别':'category',\n",
    " '开始使用化妆品的年龄' : 'category',\n",
    " '使用国货彩妆频率' : 'category',\n",
    " '国货更换时间' : 'category',\n",
    " '如何了解到国货' : 'category',\n",
    " '国货美妆的品牌':'category',\n",
    " '国货美妆的质量': 'category',\n",
    " '国货美妆购买渠道': 'category' ,\n",
    " '国货美妆的价格':'category',\n",
    " '国货美妆的问题':'category',\n",
    " '国货美妆出口优势': 'category',\n",
    " '国货美妆竞争现状': 'category' ,\n",
    " '国货美妆出口劣势':'category',\n",
    " })\n",
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    138.000000\n",
       "mean     122.637681\n",
       "std       88.612990\n",
       "min       29.000000\n",
       "25%       76.750000\n",
       "50%      101.500000\n",
       "75%      137.000000\n",
       "max      608.000000\n",
       "Name: 填写问卷时长, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['填写问卷时长'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "女    106\n",
      "男     32\n",
      "Name: 性别, dtype: int64\n",
      "18-25岁    102\n",
      "18岁以下      35\n",
      "35岁以上       1\n",
      "Name: 开始使用化妆品的年龄, dtype: int64\n",
      "偶尔使用    71\n",
      "经常使用    47\n",
      "从不使用    14\n",
      "每天使用     6\n",
      "Name: 使用国货彩妆频率, dtype: int64\n",
      "半年         59\n",
      "一年         47\n",
      "其他〖不清楚〗    18\n",
      "一个月        14\n",
      "Name: 国货更换时间, dtype: int64\n",
      "品牌推荐┋微博，小红书，抖音等APP推荐     73\n",
      "微博，小红书，抖音等APP推荐          44\n",
      "微博，小红书，抖音等APP推荐┋代购网购     11\n",
      "微博，小红书，抖音等APP推荐┋电视广告      9\n",
      "品牌推荐┋微博，小红书，抖音等APP推推荐     1\n",
      "Name: 如何了解到国货, dtype: int64\n",
      "完美日记┋花西子┋橘朵                         9\n",
      "完美日记┋花西子┋卡姿兰                        9\n",
      "完美日记┋花西子┋珀莱雅                        6\n",
      "完美日记┋橘朵┋珀莱雅                         5\n",
      "完美日记┋橘朵┋花洛莉亚                        5\n",
      "                                   ..\n",
      "完美日记┋花西子┋橘朵┋万花镜                     1\n",
      "万花镜┋卡姿兰┋戈戈舞                         1\n",
      "完美日记┋花西子┋橘朵┋卡姿兰┋珀莱雅                 1\n",
      "完美日记┋花西子┋橘朵┋卡姿兰┋花洛莉亚┋戈戈舞┋毛戈平┋珀莱雅    1\n",
      "花西子┋花洛莉亚┋毛戈平┋珀莱雅                    1\n",
      "Name: 国货美妆的品牌, Length: 82, dtype: int64\n",
      "价格┋效果┋质量          34\n",
      "价格┋成分┋效果          33\n",
      "价格┋成分┋质量          11\n",
      "价格┋效果              8\n",
      "价格┋包装┋效果           7\n",
      "成分┋效果┋质量           6\n",
      "价格┋成分┋香味           5\n",
      "价格┋质量              4\n",
      "价格┋成分              4\n",
      "成分┋包装┋效果           4\n",
      "香味┋包装┋效果           3\n",
      "效果┋质量              2\n",
      "产地┋效果              2\n",
      "价格┋成分┋包装           2\n",
      "价格┋产地┋效果           1\n",
      "价格┋香味┋包装           1\n",
      "价格┋香味┋效果           1\n",
      "价格┋香味┋质量           1\n",
      "包装┋产地┋效果           1\n",
      "包装┋效果              1\n",
      "成分┋效果              1\n",
      "价格┋成分┋其他〖对象喜欢〗     1\n",
      "成分┋质量              1\n",
      "价格┋包装┋质量           1\n",
      "香味┋产地┋质量           1\n",
      "价格┋包装              1\n",
      "香味┋质量              1\n",
      "Name: 国货美妆的质量, dtype: int64\n",
      "线上旗舰店┋电商直播间    59\n",
      "线上旗舰店          51\n",
      "线上旗舰店┋线下专柜     13\n",
      "电商直播间           8\n",
      "线下专柜            4\n",
      "电商直播间┋线下专柜      3\n",
      "Name: 国货美妆购买渠道, dtype: int64\n",
      "100-200元        87\n",
      "200－500元        38\n",
      "1000-2000元       5\n",
      "500－1000元        3\n",
      "其他〖0到100〗        2\n",
      "其他〖不一定〗          1\n",
      "其他〖低于100〗        1\n",
      "其他〖我一般就买个口红〗     1\n",
      "Name: 国货美妆的价格, dtype: int64\n",
      "品质欠佳┋价格虚高┋营销过度        21\n",
      "价格虚高┋营销过度┋假货泛滥        20\n",
      "品质欠佳                   9\n",
      "价格虚高                   8\n",
      "营销过度                   8\n",
      "品质欠佳┋价格虚高┋假货泛滥         8\n",
      "营销过度┋假货泛滥              7\n",
      "品质欠佳┋假货泛滥              6\n",
      "品质欠佳┋营销过度┋假货泛滥         6\n",
      "价格虚高┋营销过度┋产品包装廉价       6\n",
      "价格虚高┋假货泛滥              6\n",
      "价格虚高┋营销过度              4\n",
      "假货泛滥                   4\n",
      "产品包装廉价┋假货泛滥            3\n",
      "品质欠佳┋产品包装廉价            3\n",
      "品质欠佳┋营销过度              2\n",
      "营销过度┋产品包装廉价┋假货泛滥       2\n",
      "购买困难┋假货泛滥              2\n",
      "品质欠佳┋价格虚高┋产品包装廉价       2\n",
      "价格虚高┋购买困难┋假货泛滥         2\n",
      "品质欠佳┋产品包装廉价┋假货泛滥       1\n",
      "其他〖无〗                  1\n",
      "品质欠佳┋营销过度┋产品包装廉价       1\n",
      "品质欠佳┋营销过度┋其他〖提高品质〗     1\n",
      "价格虚高┋产品包装廉价            1\n",
      "产品包装廉价┋购买困难┋假货泛滥       1\n",
      "产品包装廉价┋其他〖没问题的〗        1\n",
      "购买困难                   1\n",
      "产品包装廉价                 1\n",
      "Name: 国货美妆的问题, dtype: int64\n",
      "政策支持，国产美妆品牌商巧妙的商业布局┋本土文化＋植物配方，走出专属道路    16\n",
      "包装精美，价格便宜┋本土文化＋植物配方，走出专属道路              13\n",
      "包装精美，价格便宜┋海外华人的支持，代购商的出现                13\n",
      "海外华人的支持，代购商的出现┋政策支持，国产美妆品牌商巧妙的商业布局      13\n",
      "线上渠道的助力┋本土文化＋植物配方，走出专属道路                12\n",
      "包装精美，价格便宜┋政策支持，国产美妆品牌商巧妙的商业布局           10\n",
      "包装精美，价格便宜┋线上渠道的助力                       10\n",
      "本土文化＋植物配方，走出专属道路                        10\n",
      "海外华人的支持，代购商的出现┋线上渠道的助力                   9\n",
      "包装精美，价格便宜                                8\n",
      "线上渠道的助力┋政策支持，国产美妆品牌商巧妙的商业布局              7\n",
      "线上渠道的助力                                  6\n",
      "海外华人的支持，代购商的出现┋本土文化＋植物配方，走出专属道路          5\n",
      "政策支持，国产美妆品牌商巧妙的商业布局                      4\n",
      "海外华人的支持，代购商的出现                           2\n",
      "Name: 国货美妆出口优势, dtype: int64\n",
      " 同质化经营严重          62\n",
      "各类品牌，细分市场，竞争激烈    41\n",
      "新品牌生存空间受挤压        21\n",
      "国内品牌市场份额低         14\n",
      "Name: 国货美妆竞争现状, dtype: int64\n",
      "品质欠佳┋海外化妆品市场饱和，发展困难                    17\n",
      "不符合海外消费者的化妆习惯┋海外化妆品市场饱和，发展困难           16\n",
      "品质欠佳┋不符合海外消费者的化妆习惯                     12\n",
      "不符合海外消费者的化妆习惯┋难以有效营销                   11\n",
      "难以有效营销┋海外化妆品市场饱和，发展困难                  11\n",
      "品质欠佳┋不符合海外消费者的化妆习惯┋难以有效营销              10\n",
      "品质欠佳┋不符合海外消费者的化妆习惯┋海外化妆品市场饱和，发展困难      10\n",
      "不符合海外消费者的化妆习惯┋物流运输困难                    6\n",
      "难以有效营销┋物流运输困难                           6\n",
      "不符合海外消费者的化妆习惯┋难以有效营销┋海外化妆品市场饱和，发展困难     5\n",
      "物流运输困难┋海外化妆品市场饱和，发展困难                   5\n",
      "品质欠佳┋难以有效营销┋海外化妆品市场饱和，发展困难              5\n",
      "品质欠佳┋难以有效营销                             4\n",
      "难以有效营销┋物流运输困难┋海外化妆品市场饱和，发展困难            4\n",
      "品质欠佳┋物流运输困难                             4\n",
      "不符合海外消费者的化妆习惯┋物流运输困难┋海外化妆品市场饱和，发展困难     4\n",
      "品质欠佳┋物流运输困难┋海外化妆品市场饱和，发展困难              3\n",
      "不符合海外消费者的化妆习惯┋难以有效营销┋物流运输困难             3\n",
      "品质欠佳┋不符合海外消费者的化妆习惯┋物流运输困难               1\n",
      "不符合海外消费者的化妆习惯┋其他〖杂牌太多〗                  1\n",
      "Name: 国货美妆出口劣势, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_cats(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "count    138.000000\n",
      "mean     122.637681\n",
      "std       88.612990\n",
      "min       29.000000\n",
      "25%       76.750000\n",
      "50%      101.500000\n",
      "75%      137.000000\n",
      "max      608.000000\n",
      "Name: 填写问卷时长, dtype: float64\n"
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    "fig = px.box(df5, x=\"填写问卷时长\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df5.copy()"
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  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>101</td>\n",
       "      <td>79.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男</td>\n",
       "      <td>26</td>\n",
       "      <td>20.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总和</td>\n",
       "      <td>127</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   性别   个数     百分比\n",
       "0   女  101   79.53\n",
       "1   男   26   20.47\n",
       "2  总和  127  100.00"
      ]
     },
     "execution_count": 76,
     "metadata": {},
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   ],
   "source": [
    "mytools.gen_percent_table(df,'性别')"
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  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "样本中男女比例为80：20，与总体中的51:49有差距，因美妆产品受众的原因，导致样本中的性别出现了上述偏差，但仍可使用，故可认为样本的质量没问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "品质欠佳\n",
      "价格虚高\n",
      "营销过度\n",
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      "产品包装廉价\n",
      "其他〖没问题的〗\n",
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      "价格虚高\n",
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   ],
   "source": [
    "df5['test'] = df5['国货美妆的问题'].str.split('┋')\n",
    "mcq_items = []\n",
    "for g in df5['test']:\n",
    "    # print(g)\n",
    "    for label in g:\n",
    "        print(label)\n",
    "        mcq_items.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_mcq_df(df,x,pattern='┋'):\n",
    "\n",
    "    # 按照指定分隔符将多选题字符串转化包含多个选项的列表\n",
    "    df['temp'] = df[x].str.split(pattern)\n",
    "    # 初始化列表，用于保存所有多选题选项\n",
    "    mcq_items = []\n",
    "    # 循环所有个案，获取所有多选题选项\n",
    "    for g in df['temp']:\n",
    "        for label in g:\n",
    "            # print(label)\n",
    "            mcq_items.append(label)\n",
    "    # 将多选题选项去重后转化为列表，方便构造dataframe\n",
    "    result = list(set(mcq_items))\n",
    "    # 构造包含选项、次数和比例的空表\n",
    "    df_mcq_1 = pd.DataFrame(data=np.zeros([len(result), 2]),\n",
    "                            index=result,\n",
    "                            columns=['次数', '比例'])\n",
    "    # 通过循环获取每个选项在多选题中累次出现的次数\n",
    "    for i in df[x]:\n",
    "        for label in result:\n",
    "            if str(i).__contains__(label):\n",
    "                df_mcq_1.loc[label, '次数'] += 1\n",
    "    # 生成比例列\n",
    "    df_mcq_1['比例'] = df_mcq_1['次数'] / df.shape[0] * 100\n",
    "\n",
    "    return df_mcq_1.astype({'次数':\"int\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>使用国货彩妆频率</th>\n",
       "      <th>人数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>偶尔使用</td>\n",
       "      <td>67</td>\n",
       "      <td>52.755906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>经常使用</td>\n",
       "      <td>44</td>\n",
       "      <td>34.645669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>从不使用</td>\n",
       "      <td>13</td>\n",
       "      <td>10.23622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>每天使用</td>\n",
       "      <td>3</td>\n",
       "      <td>2.362205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>127</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  使用国货彩妆频率   人数        百分比\n",
       "0     偶尔使用   67  52.755906\n",
       "1     经常使用   44  34.645669\n",
       "2     从不使用   13   10.23622\n",
       "3     每天使用    3   2.362205\n",
       "4       总和  127      100.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_df = pd.DataFrame()\n",
    "frequency_df['使用国货彩妆频率'] = df['使用国货彩妆频率'].value_counts().index\n",
    "frequency_df['人数'] = df['使用国货彩妆频率'].value_counts().values\n",
    "frequency_df['百分比'] = df['使用国货彩妆频率'].value_counts(normalize=True).values * 100\n",
    "# 新增总和行\n",
    "frequency_total_df = pd.Series({'使用国货彩妆频率':'总和','人数':frequency_df['人数'].sum(),'百分比':frequency_df['百分比'].sum()}).to_frame().T\n",
    "frequency_df = pd.concat([frequency_df,frequency_total_df],ignore_index=True)\n",
    "frequency_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'使用国货彩妆频率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'国货更换时间')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>次数</th>\n",
       "      <th>比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>微博，小红书，抖音等APP推推荐</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电视广告</th>\n",
       "      <td>8</td>\n",
       "      <td>6.299213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>代购网购</th>\n",
       "      <td>10</td>\n",
       "      <td>7.874016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品牌推荐</th>\n",
       "      <td>70</td>\n",
       "      <td>55.118110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>微博，小红书，抖音等APP推荐</th>\n",
       "      <td>126</td>\n",
       "      <td>99.212598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   次数         比例\n",
       "微博，小红书，抖音等APP推推荐    1   0.787402\n",
       "电视广告                8   6.299213\n",
       "代购网购               10   7.874016\n",
       "品牌推荐               70  55.118110\n",
       "微博，小红书，抖音等APP推荐   126  99.212598"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_app_type = gen_mcq_df(df,'如何了解到国货')\n",
    "ad_app_type = ad_app_type.sort_values(by='比例')\n",
    "ad_app_type"
   ]
  },
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   "source": [
    "fig = px.bar(ad_app_type, x=\"比例\",orientation='h')\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
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       "      <td>40.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>孔凤春</th>\n",
       "      <td>12</td>\n",
       "      <td>9.448819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>花洛莉亚</th>\n",
       "      <td>44</td>\n",
       "      <td>34.645669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>戈戈舞</th>\n",
       "      <td>27</td>\n",
       "      <td>21.259843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖半亩花田〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖杂牌，穷〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卡姿兰</th>\n",
       "      <td>45</td>\n",
       "      <td>35.433071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖我一般不用化妆品〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖薇姿〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖女朋友要啥买啥〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>万花镜</th>\n",
       "      <td>22</td>\n",
       "      <td>17.322835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖不使用〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>橘朵</th>\n",
       "      <td>73</td>\n",
       "      <td>57.480315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖不用〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>毛戈平</th>\n",
       "      <td>21</td>\n",
       "      <td>16.535433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>完美日记</th>\n",
       "      <td>85</td>\n",
       "      <td>66.929134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖品牌我也不知道〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              次数         比例\n",
       "谢馥春            3   2.362205\n",
       "花西子           49  38.582677\n",
       "其他〖其他〗         1   0.787402\n",
       "其他〖珂拉琪〗        1   0.787402\n",
       "其他〖大宝〗         1   0.787402\n",
       "其他〖无〗          1   0.787402\n",
       "珀莱雅           52  40.944882\n",
       "孔凤春           12   9.448819\n",
       "花洛莉亚          44  34.645669\n",
       "戈戈舞           27  21.259843\n",
       "其他〖半亩花田〗       1   0.787402\n",
       "其他〖杂牌，穷〗       1   0.787402\n",
       "卡姿兰           45  35.433071\n",
       "其他〖我一般不用化妆品〗   1   0.787402\n",
       "其他〖薇姿〗         1   0.787402\n",
       "其他〖女朋友要啥买啥〗    1   0.787402\n",
       "万花镜           22  17.322835\n",
       "其他〖不使用〗        1   0.787402\n",
       "橘朵            73  57.480315\n",
       "其他〖不用〗         1   0.787402\n",
       "毛戈平           21  16.535433\n",
       "完美日记          85  66.929134\n",
       "其他〖品牌我也不知道〗    1   0.787402"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_mcq_df(df,'国货美妆的品牌')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'国货美妆竞争现状')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_mcq_df(df,x,pattern='┋'):\n",
    "\n",
    "    # 按照指定分隔符将多选题字符串转化包含多个选项的列表\n",
    "    df['temp'] = df[x].str.split(pattern)\n",
    "    # 初始化列表，用于保存所有多选题选项\n",
    "    mcq_items = []\n",
    "    # 循环所有个案，获取所有多选题选项\n",
    "    for g in df['temp']:\n",
    "        for label in g:\n",
    "            # print(label)\n",
    "            mcq_items.append(label)\n",
    "    # 将多选题选项去重后转化为列表，方便构造dataframe\n",
    "    result = list(set(mcq_items))\n",
    "    # 构造包含选项、次数和比例的空表\n",
    "    df_mcq_1 = pd.DataFrame(data=np.zeros([len(result), 2]),\n",
    "                            index=result,\n",
    "                            columns=['次数', '比例'])\n",
    "    # 通过循环获取每个选项在多选题中累次出现的次数\n",
    "    for i in df[x]:\n",
    "        for label in result:\n",
    "            if str(i).__contains__(label):\n",
    "                df_mcq_1.loc[label, '次数'] += 1\n",
    "    # 生成比例列\n",
    "    df_mcq_1['比例'] = df_mcq_1['次数'] / df.shape[0] * 100\n",
    "\n",
    "    return df_mcq_1.astype({'次数':\"int\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>次数</th>\n",
       "      <th>比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>品质欠佳</th>\n",
       "      <td>56</td>\n",
       "      <td>44.094488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖没问题的〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品包装廉价</th>\n",
       "      <td>20</td>\n",
       "      <td>15.748031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖提高品质〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格虚高</th>\n",
       "      <td>73</td>\n",
       "      <td>57.480315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>假货泛滥</th>\n",
       "      <td>65</td>\n",
       "      <td>51.181102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖无〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>购买困难</th>\n",
       "      <td>6</td>\n",
       "      <td>4.724409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>营销过度</th>\n",
       "      <td>74</td>\n",
       "      <td>58.267717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          次数         比例\n",
       "品质欠佳      56  44.094488\n",
       "其他〖没问题的〗   1   0.787402\n",
       "产品包装廉价    20  15.748031\n",
       "其他〖提高品质〗   1   0.787402\n",
       "价格虚高      73  57.480315\n",
       "假货泛滥      65  51.181102\n",
       "其他〖无〗      1   0.787402\n",
       "购买困难       6   4.724409\n",
       "营销过度      74  58.267717"
      ]
     },
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       "      <th>本土文化＋植物配方，走出专属道路</th>\n",
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       "      <th>政策支持，国产美妆品牌商巧妙的商业布局</th>\n",
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       "                     次数         比例\n",
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     "metadata": {},
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
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       "    <tr>\n",
       "      <th>包装精美，价格便宜</th>\n",
       "      <td>49</td>\n",
       "      <td>38.582677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>政策支持，国产美妆品牌商巧妙的商业布局</th>\n",
       "      <td>49</td>\n",
       "      <td>38.582677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>本土文化＋植物配方，走出专属道路</th>\n",
       "      <td>54</td>\n",
       "      <td>42.519685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     次数         比例\n",
       "海外华人的支持，代购商的出现       38  29.921260\n",
       "线上渠道的助力              41  32.283465\n",
       "包装精美，价格便宜            49  38.582677\n",
       "政策支持，国产美妆品牌商巧妙的商业布局  49  38.582677\n",
       "本土文化＋植物配方，走出专属道路     54  42.519685"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_app_type = gen_mcq_df(df,'国货美妆出口优势')\n",
    "ad_app_type = ad_app_type.sort_values(by='比例')\n",
    "ad_app_type"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
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    "fig.show()"
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  },
  {
   "cell_type": "code",
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       "      <td>50.393701</td>\n",
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       "    <tr>\n",
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       "      <td>58.267717</td>\n",
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       "      <th>难以有效营销</th>\n",
       "      <td>51</td>\n",
       "      <td>40.157480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海外化妆品市场饱和，发展困难</th>\n",
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       "      <td>59.055118</td>\n",
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       "                次数         比例\n",
       "品质欠佳            64  50.393701\n",
       "不符合海外消费者的化妆习惯   74  58.267717\n",
       "物流运输困难          32  25.196850\n",
       "其他〖杂牌太多〗         1   0.787402\n",
       "难以有效营销          51  40.157480\n",
       "海外化妆品市场饱和，发展困难  75  59.055118"
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     },
     "execution_count": 93,
     "metadata": {},
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   ],
   "source": [
    "gen_mcq_df(df,'国货美妆出口劣势')"
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  {
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   "execution_count": 94,
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       "      <th>次数</th>\n",
       "      <th>比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>其他〖杂牌太多〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
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       "      <td>51</td>\n",
       "      <td>40.157480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品质欠佳</th>\n",
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       "      <td>50.393701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不符合海外消费者的化妆习惯</th>\n",
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       "                次数         比例\n",
       "其他〖杂牌太多〗         1   0.787402\n",
       "物流运输困难          32  25.196850\n",
       "难以有效营销          51  40.157480\n",
       "品质欠佳            64  50.393701\n",
       "不符合海外消费者的化妆习惯   74  58.267717\n",
       "海外化妆品市场饱和，发展困难  75  59.055118"
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     },
     "execution_count": 94,
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   "source": [
    "ad_app_type = gen_mcq_df(df,'国货美妆出口劣势')\n",
    "ad_app_type = ad_app_type.sort_values(by='比例')\n",
    "ad_app_type"
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          "font": {
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          "geo": {
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           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
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          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
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           "style": "light"
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          "paper_bgcolor": "white",
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           "angularaxis": {
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           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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         "title": {
          "text": "比例"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "index"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(ad_app_type, x=\"比例\",orientation='h')\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'国货美妆购买渠道')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>次数</th>\n",
       "      <th>比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>谢馥春</th>\n",
       "      <td>3</td>\n",
       "      <td>2.362205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>花西子</th>\n",
       "      <td>49</td>\n",
       "      <td>38.582677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖其他〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖珂拉琪〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖大宝〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖无〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>珀莱雅</th>\n",
       "      <td>52</td>\n",
       "      <td>40.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>孔凤春</th>\n",
       "      <td>12</td>\n",
       "      <td>9.448819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>花洛莉亚</th>\n",
       "      <td>44</td>\n",
       "      <td>34.645669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>戈戈舞</th>\n",
       "      <td>27</td>\n",
       "      <td>21.259843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖半亩花田〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖杂牌，穷〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卡姿兰</th>\n",
       "      <td>45</td>\n",
       "      <td>35.433071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖我一般不用化妆品〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖薇姿〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖女朋友要啥买啥〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>万花镜</th>\n",
       "      <td>22</td>\n",
       "      <td>17.322835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖不使用〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>橘朵</th>\n",
       "      <td>73</td>\n",
       "      <td>57.480315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他〖不用〗</th>\n",
       "      <td>1</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>毛戈平</th>\n",
       "      <td>21</td>\n",
       "      <td>16.535433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>完美日记</th>\n",
       "      <td>85</td>\n",
       "      <td>66.929134</td>\n",
       "    </tr>\n",
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       "      <th>其他〖品牌我也不知道〗</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "              次数         比例\n",
       "谢馥春            3   2.362205\n",
       "花西子           49  38.582677\n",
       "其他〖其他〗         1   0.787402\n",
       "其他〖珂拉琪〗        1   0.787402\n",
       "其他〖大宝〗         1   0.787402\n",
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       "孔凤春           12   9.448819\n",
       "花洛莉亚          44  34.645669\n",
       "戈戈舞           27  21.259843\n",
       "其他〖半亩花田〗       1   0.787402\n",
       "其他〖杂牌，穷〗       1   0.787402\n",
       "卡姿兰           45  35.433071\n",
       "其他〖我一般不用化妆品〗   1   0.787402\n",
       "其他〖薇姿〗         1   0.787402\n",
       "其他〖女朋友要啥买啥〗    1   0.787402\n",
       "万花镜           22  17.322835\n",
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       "完美日记          85  66.929134\n",
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     },
     "execution_count": 98,
     "metadata": {},
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   ],
   "source": [
    "gen_mcq_df(df,'国货美妆的品牌')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
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       "              次数         比例\n",
       "其他〖杂牌，穷〗       1   0.787402\n",
       "其他〖不用〗         1   0.787402\n",
       "其他〖不使用〗        1   0.787402\n",
       "其他〖女朋友要啥买啥〗    1   0.787402\n",
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       "其他〖我一般不用化妆品〗   1   0.787402\n",
       "其他〖半亩花田〗       1   0.787402\n",
       "其他〖品牌我也不知道〗    1   0.787402\n",
       "其他〖无〗          1   0.787402\n",
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   "source": [
    "ad_app_type = gen_mcq_df(df,'国货美妆的品牌')\n",
    "ad_app_type = ad_app_type.sort_values(by='比例')\n",
    "ad_app_type"
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       "           次数         比例\n",
       "其他〖对象喜欢〗    1   0.787402\n",
       "产地          4   3.149606\n",
       "香味         10   7.874016\n",
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       "成分         63  49.606299\n",
       "效果         97  76.377953\n",
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    "ad_app_type = gen_mcq_df(df,'国货美妆的质量')\n",
    "ad_app_type = ad_app_type.sort_values(by='比例')\n",
    "ad_app_type"
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    "fig = px.bar(ad_app_type, x=\"比例\",orientation='h')\n",
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    "ad_app_type = ad_app_type.sort_values(by='比例')\n",
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          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
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          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
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          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "colorscale": [
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              0,
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            "type": "surface"
           }
          ],
          "table": [
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            "cells": {
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              "color": "#EBF0F8"
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             "line": {
              "color": "white"
             }
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            "header": {
             "fill": {
              "color": "#C8D4E3"
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             "line": {
              "color": "white"
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            },
            "type": "table"
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         },
         "layout": {
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           "arrowwidth": 1
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          "colorscale": {
           "diverging": [
            [
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             0.2,
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             0.3,
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          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "比例"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "index"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(ad_app_type, x=\"比例\",orientation='h')\n",
    "fig.show()\n"
   ]
  }
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